Research on Dynamic Evolutionary Efficiency and Regional Differentiation of High-Tech Industrial Chain Networks
Abstract
:1. Introduction
2. Literature Review
- (1)
- Research on factors influencing the innovation efficiency of high-tech industries. Some scholars believe that factors such as enterprise size, industry concentration, enterprise openness, government support, R&D investment, etc. will have a certain impact on the innovation efficiency of high-tech enterprises [8,9,10,11]. In recent years, some scholars have also explored the role of digital economy, green finance and other factors in improving high-tech industries, such as Ying et al. [12], who believe that the digital economy promotes the improvement of the industrial innovation efficiency of high-tech enterprises through strengthening entrepreneurship and industrial structure upgrading, and Wang [13], who argues that there is industry heterogeneity in terms of the impact of green financial policies on the R&D efficiency of technological innovations in high-tech industries.
- (2)
- Research on evaluation of innovation efficiency of high-tech industries. Scholars at home and abroad mostly use hierarchical analysis, stochastic frontier analysis (SFA) and data envelopment analysis (DEA) to carry out assessments. Among them, the DEA method is more commonly used by many scholars, such as Ma et al. [14] who measured the innovation efficiency of listed companies based on the DEA method, and concluded that insufficient innovation investment is the main reason affecting the innovation efficiency. Peng et al. [15] assessed the innovation efficiency of science and technology enterprises in Hebei province in different regions and time nodes by constructing the SBM-Malmquist model. Some other scholars evaluate the innovation efficiency of high-tech industries by distinguishing the research samples, such as Kim et al. [16] and Liu et al. [17] who conducted empirical studies from the perspective of the structure of capital investment and from the perspective of the industry, respectively.
3. Research Methods
3.1. Three-Stage DDF Dynamic Model
3.1.1. Objective Function
3.1.2. Period and Division Efficiencies
3.2. Input, Desirable Output, and Undesirable Output Efficiency
4. Results and Discussion
4.1. Variables and Data
4.1.1. Variable Interpretation
- (a)
- R&D personnel are indicators used to measure the input of scientific and technological human resources, such as R&D full-time personnel (persons whose cumulative working hours engaged in R&D activities throughout the year account for 90% or more of their total working hours) workload and incomplete. The sum of the work is equivalent to the hourly personnel converted according to actual working hours.
- (b)
- R&D expenditure refers to the funds used for basic research, applied research, and experimental development. Specifically, it refers to the costs incurred by enterprises in the research and development process of products, technologies, materials, and processes.
- (c)
- The number of invention patents refers to the number of patents approved and authorized by the State Intellectual Property Office.
- (d)
- New product development is the actual number of product produced during the applied innovation research process.
- (e)
- Non-R&D personnel refers to scientific and technological personnel other than R&D scientists and engineers. This study uses year-end practitioners minus R&D personnel.
- (f)
- Non-R&D expenditure is the expenditure of enterprises in the process of scientific research and experimental development for the introduction of technology, digestion and absorption, purchase of domestic technology, and technological transformation.
- (g)
- New product development cost is usually measured by the amount of financial support in the process of new product development.
- (h)
- The number of new projects reflects the actual project results after the investment.
- (i)
- Utility model patents are the total number of new practical technical solutions proposed for the shape and structure of a product.
- (j)
- Engaged personnel refers to the number of non-R&D personnel, minus the number of R&D personnel, from the total number of employees at the end of the year.
- (k)
- Operating expenses refer to sales expenses—that is, various expenses incurred in the process of selling goods and materials and providing labor services. This study uses main business income minus total profit to measure operating expenses.
- (l)
- New product sales income reflects the economic value of innovative products in the market.
- (m)
- Main business income refers to income from the main business.
- (n)
- Fixed assets are tangible assets held by the high-tech industry for the production of products, the provision of labor services, leasing, or operations and management. Their service life exceeds the end of one fiscal year.
4.1.2. Data Description
4.2. Analysis of Total Efficiency of the High-Tech Industry
4.3. Analysis on Three-Stage Efficiency of the High-Tech Industry
4.3.1. Basic Innovation Efficiency Analysis
4.3.2. Efficiency Analysis of the Applied Innovation Stage
4.3.3. Efficiency Analysis of the Profitable Innovation Stage
4.3.4. Comparison and Analysis of Three-Stage Efficiency of the High-Tech Industry
4.4. Annual Efficiency Analysis of the Main Variables of the High-Tech Industry
5. Conclusions and Policy Recommendations
5.1. Empirical Analysis Conclusions
- (1)
- From the three-stage total efficiency value of China’s high-tech industries in various regions, Beijing, Qinghai, Hainan, and Guangdong have been 1 for five years. The overall scores of most provinces in the three stages in 2015–2019 are on the rise, but the overall efficiency values of Shanghai, Shanxi, and Gansu are showing a downward trend. The total efficiency value of the three stages from 2015 to 2019 in the western region is higher than that in the eastern and central regions. The central region has the lowest overall efficiency value among the three regions. The input variables in the western region are always the lowest, but its efficiency value is higher than that in the central region. The level of innovation input and output in the western region is relatively high.
- (2)
- From the three-stage efficiency, most of China’s 30 provinces exhibit basic innovation efficiency < applied innovation efficiency < profitable innovation efficiency. However, Jiangxi, Shanxi, and Guangxi show that basic innovation efficiency < profitable innovation efficiency < applied innovation efficiency, which relates to local high-tech industrial policies, production economic activities, and operating standards. The application innovation efficiency value of Fujian is lower than basic innovation and income innovation, which shows that the application level of scientific and technological innovation in Fujian needs to be improved. The efficiency of basic innovation and applied innovation of Jiangxi is 1 in 2016–2019, while its efficiency of profitable innovation is low. The efficiency of profitable innovation in Guangxi and Shanxi has shown a downward trend in the past five years. Beijing, Guangdong, Hainan, and Qinghai all have an efficiency value of 1 in the three stages in the past five years, which closely relate to their economic development level and technological innovation policies. The efficiency of basic innovation in the eastern region is higher than that in the central and western regions. The basic innovation efficiency of the central region is the lowest among the three regions. The input-output effect of basic innovation in China’s high-tech industry thus needs to be improved. The efficiency values of the applied innovation stage and the profitable innovation stage are generally higher. This reflects that China’s current high-tech industries are relatively complete, and its industrial operation activities are also relatively complete.
- (3)
- From the first stage, the efficiency values of R&D personnel, number of invention patents, and new product development in the basic innovation stage of most provinces in China are 1. In addition to a slight decline in Guangxi and other regions, other provinces have shown an upward trend. The research and development efficiency of R&D personnel in China’s high-tech industries is relatively high. In terms of R&D expenditure efficiency, the efficiency value of most provinces is between 0.8–0.9, and meaning that there is room for improvement in the R&D expenditure of China’s high-tech industries.
- (4)
- The efficiency values of non-R&D personnel and number of new projects in the second stage are mostly 1, but the efficiency of Hebei and Henan is showing a downward trend, while the efficiency of the remaining provinces is increasing. This shows that the efficiency of technology application in China’s high-tech industries in various regions is relatively high. The non-R&D expenditure efficiency values of most regional input indicators in China are between 0.7 and 1.0, while the efficiency values of Fujian are below 0.4. The region has insufficient non-R&D expenditure input. The efficiency value of utility model patents has a large regional difference, and the efficiency value of some provinces is below 0.5. The research and development skills of utility model patents in innovative applications in China’s provinces are not yet mature.
- (5)
- The calendar year efficiency values of the input indicator operating expenses and the output indicator main business income of the third stage of gainful innovation stage in all provinces of China are all 1, and they are relatively stable, which reflects that China’s high-tech industry currently plays a more significant role in economic development. In addition, in terms of the input variable engaged personnel, the fluctuation of the efficiency value of some provinces is larger, as the efficiency value of the engaged personnel in Inner Mongolia has significantly decreased from 1 in 2015 to 0.4493 in 2019. This indicates that the scale of the engaged personnel in high-tech industries in these regions cannot support their industrial development. In terms of the output variable, new product sales revenue, the efficiency value of most provinces is between 0.7 and 1.0, except for Shaanxi and Heilongjiang provinces, where the efficiency value shows a downward trend, and the rest of the provinces show an upward trend.
5.2. Policy Recommendations
- (1)
- Investment should be increased in technological innovation, and basic innovation efficiency must be improved. At present, the overall level of basic innovation efficiency of China’s high-tech industries is relatively low. It is thus necessary to improve the input mechanism for scientific and technological innovation, continuously broaden the investment and financing channels for high-tech industries, actively encourage and guide financial institutions and private funds to participate in scientific and technological innovation, establish a sound production–university–research cooperation development mechanism for high-tech industries, and introduce scientific researchers from universities or scientific research institutes to join the innovative activities of high-tech industries.
- (2)
- The government should guide high-tech enterprises to strengthen independent innovation management and focus on the research and development of core technologies, make full use of public resources, give policy support to high-tech enterprises, guide enterprises to strengthen their focus on R&D departments, and give priority to R&D departments in terms of funding arrangements. At the same time, the government should pay attention to the cooperation and exchange between high-tech industries in various regions, promote coordinated development, and reduce the efficiency of high-tech innovation between regions. Regions with low innovation efficiency should learn from high-efficiency regions, make up for the shortcomings in their own development, and improve innovation efficiency. In terms of resource allocation, the central government should pay attention to the rational allocation and planning of resources, promote open cooperation among regions, and jointly build resource sharing mechanisms covering industry, finance, talents, technology, and information, so as to achieve complementary advantages while promoting regional coordinated development.
- (3)
- For developing countries, they should tailor their policies to the local conditions and target the key issues that are dragging down their overall efficiency. Since there are big differences in resource factor endowment, economic development level and industrial structure of each country, the innovation efficiency of high-tech industry in each country has different key problems. For countries with low basic innovation efficiency, they should build a diversified basic innovation investment mechanism, increase government investment, encourage enterprises to play the main role in basic innovation, and widely absorb social funds. Meanwhile, due to the problems of large investment in basic innovation, long period of time, inconspicuous economic benefits, and strong externality, they should strengthen the international cooperation in the field of basic innovation in order to maximize the benefits. For countries with a low applied innovation efficiency value, it is necessary to provide the ability to apply science and technology innovation, optimize the innovation development environment, reduce the innovation cost of enterprises and improve the input-output ratio of innovation activities by promoting the construction of the legal environment, the basic knowledge and technology environment as well as the infrastructure environment such as road traffic. Countries with low innovation efficiency in high-tech industries need to focus on the industrial characteristics and local advantages of high-tech industries and on improving the technological income-generating efficiency of enterprises.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Region | Statistical Indicators | R&D Personnel | R&D Expenditure | Number of Invention Patents | New Product Development | Non-R&D Personnel | Non-R&D Expenditure | New Product Development |
China | Average | 26,064.78 | 1,056,169.74 | 11,917.85 | 3649.24 | 786,163.28 | 250,123.08 | 1,330,913.65 |
Max | 286,009.81 | 11,247,028.33 | 206,134.13 | 38,526.12 | 6,521,815.21 | 5,184,499.12 | 17,862,113.41 | |
Min | 82.12 | 4143.13 | 13.21 | 24.32 | 30,740.31 | 1431.55 | 8853.12 | |
St.Dev | 46,483.67269 | 1,911,188.295 | 32,664.40 | 6175.63 | 994,348.59 | 608,314.31 | 2,833,244.92 | |
East | Average | 54,183.28 | 2,219,495.47 | 27,404.35 | 7497.33 | 1,484,470.66 | 537,432.39 | 2,883,937.41 |
Max | 286,009.81 | 11,247,028.12 | 206,134.12 | 38,526.13 | 6,521,815.43 | 5,184,499.95 | 17,862,114.36 | |
Min | 824.31 | 37,340.93 | 560.12 | 348.32 | 30,930.16 | 11,267.35 | 40,853.71 | |
St.Dev | 67,324.29 | 2,760,685.61 | 50,382.06 | 8855.49 | 1,339,356.15 | 932,381.53 | 4,228,966.49 | |
Central | Average | 14,055.27 | 529,042.53 | 3917.46 | 2048.15 | 555,294.88 | 103,775.13 | 592,759.02 |
Max | 27,265.73 | 1,454,446.75 | 10,060.91 | 4695.17 | 1,179,966.88 | 415,383.54 | 1,598,880.53 | |
Min | 2237.14 | 42,822.87 | 585.51 | 325.28 | 165,517.59 | 10,325.15 | 53,317.57 | |
St.Dev | 8735.56 | 398,637.13 | 3191.72 | 1247.02 | 297,155.63 | 104,554.91 | 447,589.97 | |
West | Average | 6680.48 | 276,209.28 | 2249.85 | 965.58 | 255,760.17 | 69,248.65 | 314,729.66 |
Max | 33,144.33 | 1,394,285.13 | 13,761.25 | 4921.29 | 813,312.69 | 413,880.41 | 1,835,505.13 | |
Min | 82.21 | 4143.33 | 13.14 | 24.92 | 30,740.41 | 1431.55 | 8853.94 | |
St.Dev | 8916.84 | 372,305.42 | 3381.12 | 1172.36 | 218,699.44 | 102,769.33 | 456,710.45 | |
Region | Statistical Indicators | Number of New Projects | Utility Model Patents | Engaged Personnel | Operating Expenses | New Product Sales Income | Main Business Income | |
China | Average | 587.04 | 123,379.45 | 794,342.45 | 4763.78 | 17,044,869.66 | 5098.11 | |
Max | 3452.21 | 960,233.31 | 6,688,894.12 | 44,405.93 | 208,509,695.92 | 46,747.47 | ||
Min | 14.13 | 1557.57 | 30,888.31 | 65.25 | 55,221.17 | 72.29 | ||
St.Dev | 677.69 | 161,574.37 | 1,008,475.72 | 8186.36 | 34,759,294.05 | 8671.22 | ||
East | Average | 815.82 | 97,333.15 | 1,502,238.35 | 9323.41 | 35,820,460.95 | 9974.32 | |
Max | 3452.48 | 732,163.59 | 6,688,894.25 | 44,405.93 | 208,509,696.54 | 46,747.32 | ||
Min | 14.25 | 3072.72 | 31,585.64 | 135.58 | 82,804.95 | 156.02 | ||
St.Dev | 942.09 | 134,182.07 | 1,359,471.55 | 12,010.89 | 51,467,767.95 | 12,695.12 | ||
Central | Average | 705.94 | 192,699.58 | 559,041.64 | 2907.43 | 10,121,533.23 | 3119.3 | |
Max | 1514.25 | 584,454.24 | 1,185,250.34 | 6957.23 | 36,536,924.11 | 7402.04 | ||
Min | 72.21 | 2953.34 | 165,039.36 | 373.81 | 387,785.19 | 418.08 | ||
St.Dev | 445.34 | 163,022.83 | 298,752.52 | 1855.01 | 10,321,747.14 | 1963.67 | ||
West | Average | 271.77 | 99,011.13 | 257,574.41 | 1554.23 | 3,304,432.06 | 1661.69 | |
Max | 991.19 | 960,233.31 | 829,788.14 | 6564.65 | 17,996,612.24 | 6942.89 | ||
Min | 22.12 | 1557.51 | 30,888.25 | 65.55 | 55,221.31 | 72.21 | ||
St.Dev | 267.37 | 173,146.77 | 220,778.71 | 1987.34 | 4,586,107.81 | 2093.37 |
Rank | DMU | 2015 | 2016 | 2017 | 2018 | 2019 |
---|---|---|---|---|---|---|
1 | Shanghai | 0.8317 | 0.9024 | 0.8526 | 0.7907 | 0.7921 |
2 | Shanxi | 0.9329 | 0.8443 | 0.8615 | 0.8796 | 0.8803 |
3 | Shandong | 0.7159 | 0.8215 | 0.7609 | 0.8029 | 0.8210 |
4 | Inner Mongolia | 0.6728 | 0.7249 | 0.9649 | 0.8531 | 0.8537 |
5 | Tianjin | 0.8096 | 0.8839 | 0.7937 | 0.8903 | 0.8903 |
6 | Beijing | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 |
7 | Sichuan | 0.7577 | 0.7861 | 0.7916 | 0.8468 | 0.8837 |
8 | Gansu | 0.9397 | 0.8889 | 0.7363 | 0.6582 | 0.6926 |
9 | Jilin | 0.9729 | 0.9653 | 0.9356 | 0.9901 | 0.9901 |
10 | Anhui | 0.8626 | 0.9545 | 0.8557 | 0.8260 | 0.8390 |
11 | Jiangxi | 0.8157 | 0.9963 | 1.0000 | 0.9920 | 0.9904 |
12 | Jiangsu | 0.7305 | 0.8189 | 0.7623 | 0.8720 | 0.8720 |
13 | Hebei | 0.6813 | 0.7062 | 0.6998 | 0.7308 | 0.7208 |
14 | Henan | 0.6962 | 0.6909 | 0.6807 | 0.7979 | 0.7692 |
15 | Qinghai | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 |
16 | Chongqing | 0.8041 | 1.0000 | 1.0000 | 0.9025 | 0.8675 |
17 | Hainan | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 |
18 | Zhejiang | 0.8776 | 0.8910 | 0.8579 | 0.8805 | 0.8477 |
19 | Shaanxi | 0.5701 | 0.5916 | 0.6291 | 0.7453 | 0.7329 |
20 | Hubei | 0.6459 | 0.7623 | 0.6677 | 0.7923 | 0.7829 |
21 | Hunan | 0.7760 | 0.6609 | 0.6509 | 0.7277 | 0.7302 |
22 | Guizhou | 0.7492 | 0.9639 | 0.8661 | 0.9766 | 0.9729 |
23 | Yunnan | 0.9974 | 1.0000 | 0.9817 | 0.9319 | 0.8923 |
24 | Heilongjiang | 0.8570 | 0.7290 | 0.7726 | 0.9804 | 0.9795 |
25 | Xinjiang | 0.9507 | 0.8114 | 0.8875 | 0.9966 | 0.9966 |
26 | Ningxia | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 0.9938 |
27 | Fujian | 0.6137 | 0.7319 | 0.7965 | 0.9261 | 0.9484 |
28 | Guangxi | 0.9151 | 0.9551 | 0.9626 | 0.9112 | 0.9112 |
29 | Guangdong | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 |
30 | Liaoning | 0.7148 | 0.7855 | 0.8762 | 0.8481 | 0.8403 |
DMU | R&D Personnel | R&D Expenditure | Non-R&D Personnel | Non-R&D Expenditure | New Product Development Cost | Engaged Personnel | Operating Expenses |
Shanghai | 0.9891 | 0.9902 | 1 | 1 | 0.9592 | 0.9891 | 0.9901 |
Shanxi | 0.8166 | 0.9286 | 0.9182 | 1 | 1 | 0.8166 | 0.9286 |
Shandong | 1 | 0.8865 | 1 | 0.8371 | 0.8475 | 1 | 0.8865 |
Inner Mongolia | 1 | 0.5502 | 0.9310 | 0.8687 | 0.9852 | 1 | 0.5502 |
Tianjin | 0.9733 | 0.9532 | 1 | 1 | 1 | 0.9734 | 0.9532 |
Beijing | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
Sichuan | 0.9646 | 0.8788 | 1 | 0.8542 | 0.9571 | 0.9647 | 0.8788 |
Gansu | 0.9153 | 0.8752 | 0.9808 | 1 | 1 | 0.9153 | 0.8752 |
Jilin | 0.9983 | 0.9869 | 1 | 1 | 1 | 0.9983 | 0.9869 |
Anhui | 0.9651 | 0.9405 | 1 | 0.9713 | 0.9366 | 0.9651 | 0.9405 |
Jiangxi | 1 | 1 | 1 | 0.8560 | 0.9400 | 1 | 1 |
Jiangsu | 0.9601 | 0.9172 | 1 | 0.8340 | 0.7752 | 0.9601 | 0.9172 |
Hebei | 0.9473 | 0.9577 | 0.9616 | 1 | 1 | 0.9473 | 0.9577 |
Henan | 0.9051 | 1 | 0.9693 | 1 | 0.9822 | 0.9051 | 1 |
Qinghai | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
Chongqing | 1 | 0.9774 | 1 | 1 | 0.9648 | 1 | 0.9774 |
Hainan | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
Zhejiang | 0.9042 | 0.9812 | 1 | 1 | 1 | 0.9042 | 0.9812 |
Shaanxi | 0.9662 | 1 | 1 | 0.7897 | 0.7905 | 0.9662 | 1 |
Hubei | 1 | 0.8326 | 1 | 0.9961 | 0.8803 | 1 | 0.8326 |
Hunan | 0.9469 | 1 | 1 | 0.7020 | 0.9284 | 0.9469 | 1 |
Guizhou | 0.9532 | 0.9409 | 1 | 0.7043 | 0.9148 | 0.9532 | 0.9409 |
Yunnan | 0.9785 | 1 | 0.9151 | 1 | 1 | 0.9785 | 1 |
Heilongjiang | 0.9390 | 0.9774 | 1 | 0.9979 | 0.9492 | 0.9390 | 0.9774 |
Xinjiang | 1 | 0.8327 | 0.8926 | 0.9380 | 1 | 1 | 0.8327 |
Ningxia | 1 | 0.9077 | 1 | 1 | 1 | 1 | 0.9077 |
Fujian | 0.9998 | 0.9417 | 1 | 0.4304 | 0.7355 | 0.9998 | 0.9417 |
Guangxi | 0.9591 | 1 | 1 | 1 | 1 | 0.9591 | 1 |
Guangdong | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
Liaoning | 0.9438 | 0.9619 | 0.9243 | 0.7574 | 1 | 0.9438 | 0.9619 |
DMU | New Product Sales Income | Main Business Income | Number of Invention Patents | New Product Development | Number of New Projects | Utility Model Patents | |
Shanghai | 0.5986 | 1 | 1 | 0.9728 | 0.3407 | 0.6254 | |
Shanxi | 0.4874 | 1 | 1 | 1 | 1 | 0.8890 | |
Shandong | 0.9285 | 1 | 0.9746 | 1 | 0.9785 | 0.2055 | |
Inner Mongolia | 0.9841 | 1 | 0.8766 | 1 | 1 | 1 | |
Tianjin | 1 | 1 | 1 | 1 | 1 | 0.8071 | |
Beijing | 1 | 1 | 1 | 1 | 1 | 1 | |
Sichuan | 0.8157 | 1 | 1 | 0.9921 | 1 | 0.5412 | |
Gansu | 1 | 1 | 1 | 1 | 1 | 1 | |
Jilin | 1 | 1 | 1 | 1 | 1 | 1 | |
Anhui | 1 | 1 | 1 | 1 | 1 | 0.6967 | |
Jiangxi | 0.7170 | 1 | 0.9432 | 1 | 1 | 0.9133 | |
Jiangsu | 0.9298 | 1 | 0.9986 | 1 | 1 | 0.4314 | |
Hebei | 1 | 1 | 0.9518 | 1 | 0.5537 | 0.1499 | |
Henan | 1 | 1 | 0.9254 | 1 | 1 | 1 | |
Qinghai | 1 | 1 | 1 | 1 | 1 | 1 | |
Chongqing | 0.9882 | 1 | 0.8907 | 1 | 1 | 0.8373 | |
Hainan | 1 | 1 | 1 | 1 | 1 | 1 | |
Zhejiang | 1 | 1 | 0.9745 | 1 | 0.9411 | 0.8144 | |
Shaanxi | 0.7077 | 1 | 1 | 1 | 1 | 0.6088 | |
Hubei | 0.9787 | 1 | 1 | 0.9854 | 0.9285 | 0.2993 | |
Hunan | 1 | 0.9981 | 1 | 1 | 1 | 0.7885 | |
Guizhou | 0.6831 | 1 | 1 | 1 | 0.9355 | 0.0971 | |
Yunnan | 0.8410 | 1 | 1 | 0.9626 | 0.7846 | 0.7921 | |
Heilongjiang | 0.7211 | 1 | 0.9569 | 1 | 1 | 0.8323 | |
Xinjiang | 1 | 1 | 0.9940 | 1 | 0.8992 | 0.9444 | |
Ningxia | 1 | 1 | 1 | 1 | 1 | 1 | |
Fujian | 0.9946 | 1 | 0.9223 | 1 | 1 | 0.7822 | |
Guangxi | 0.7955 | 1 | 1 | 1 | 1 | 1 | |
Guangdong | 1 | 1 | 1 | 1 | 1 | 1 | |
Liaoning | 1 | 1 | 1 | 0.9159 | 0.6029 | 0.7549 |
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Chen, L.; Xiao, Q.; Wang, J.; Fang, Z. Research on Dynamic Evolutionary Efficiency and Regional Differentiation of High-Tech Industrial Chain Networks. Sustainability 2023, 15, 16643. https://doi.org/10.3390/su152416643
Chen L, Xiao Q, Wang J, Fang Z. Research on Dynamic Evolutionary Efficiency and Regional Differentiation of High-Tech Industrial Chain Networks. Sustainability. 2023; 15(24):16643. https://doi.org/10.3390/su152416643
Chicago/Turabian StyleChen, Lihui, Qiqi Xiao, Jianlin Wang, and Zhong Fang. 2023. "Research on Dynamic Evolutionary Efficiency and Regional Differentiation of High-Tech Industrial Chain Networks" Sustainability 15, no. 24: 16643. https://doi.org/10.3390/su152416643